Nonparametric Regression Analysis of Longitudinal Data

Nonparametric approaches have recently emerged as a flexible way to model longitudinal data. This entry reviews some of the common nonparametric approaches to incorporate time and other covariate effects for longitudinally observed response data. Smoothing procedures are invoked to estimate the associated nonparametric functions, but the choice of smoothers can vary and is often subjective. Both fixed and random effects may be included for vector or longitudinal covariates. A closely related type of data is functional data, where the prevailing approaches to model random effects are through functional principal components analysis and B-splines. Related semi-parametric regression models also play an increasingly important role.